Abu Dhabi is to launch a dedicated artificial intelligence research centre to help cement the UAE's growing status as a global centre for technological innovation. The state-of-the-art complex will be central to the work of the capital's Technology Innovation Institute, which already is home to the Middle East's first quantum computer and to teams of researchers developing advanced materials, drones and robots for commercial use. The centre aims to bridge the gap between the research centre's seven labs and the spreading field of AI, providing oversight and technical know-how. Take, for example, an autonomous boat under development at TII's robotics lab, which is being designed to self-navigate to the site of an oil spill, send out dozens of robotic "fish" to assess the damage to marine life, all while sending information to drones hovering above to determine a course for clean-up. This scenario relies heavily on AI capabilities and is one of dozens of commercial projects being developed at TII's Masdar City campus.

Instead of computing the success or failure of AI initiatives on a project-by-project basis, companies using the portfolio approach compute the ROI for all their AI initiatives. A portfolio approach works in other areas of business, and the same principles apply here. Take a look at three relevant examples and the lessons for AI. In the pharmaceutical world, developing a new drug takes an average of at least ten years and costs over $2.6 billion. Literally thousands and even millions of molecules and investigative drugs are studied during the initial drug discovery and preclinical trial phases of the R&D process.

Across industries, companies are applying artificial intelligence to their businesses, with mixed results. "What separates the AI projects that succeed from the ones that don't often has to do with the business strategies organizations follow when applying AI," writes Wharton professor of operations, information and decisions Kartik Hosanagar in this opinion piece. Hosanagar is faculty director of Wharton AI for Business, a new Analytics at Wharton initiative that will support students through research, curriculum, and experiential learning to investigate AI applications. He also designed and instructs Wharton Online's Artificial Intelligence for Business course. While many people perceive artificial intelligence to be the technology of the future, AI is already here.

I argue that the most interesting goal facing researchers in automated reasoning is being able to solve problems that cannot currently be solved by existing tools and methods. This may appear obvious, and is clearly not an original thought, but focusing on this as a primary goal allows us to examine other goals in a new light. Many successful theorem provers employ a portfolio of different methods for solving problems. This changes the landscape on which we perform our research: solving problems that can already be solved may not improve the state of the art and a method that can solve a handful of problems unsolvable by current methods, but generally performs poorly on most problems, can be very useful. We acknowledge that forcing new methods to compete against portfolio solvers can stifle innovation. However, this is only the case when comparisons are made at the level of total problems solved. We propose a movement towards focussing on unique solutions in evaluation and competitions i.e. measuring the potential contribution to a portfolio solver. This state of affairs is particularly prominent in first-order logic, which is undecidable. When reasoning in a decidable logic there can be a focus on optimising a decision procedure and measuring average solving times. But in a setting where solutions are difficult to find, average solving times lose meaning, and whilst improving the efficiency of a technique can move potential solutions within acceptable time limits, in general, complementary strategies may be more successful.

This seems like an obvious one, but with so many potential areas for AI exploration, starting with the right projects--and stakeholders--is crucial for long-term success. First and foremost, the process of identifying and selecting use cases shouldn't be driven by technology alone. That is, you don't want to think about AI solely in terms of where you can apply natural language processing, for example, or how you can leverage a labeled data set. Instead, ask where you seek to increase productivity or derive new value. Going through the questioning exercise above with the various leaders who may own or touch AI, such as the chief information officer, chief digital officer, chief data scientist, and other specialists (see #3), will enable you to identify where to start.

In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute -- without learning an explicit model -- a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of sunny-csp conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.

Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specification. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP. We also report some empirical results showing that the performances that can be obtained using these features are effective and competitive with state of the art CSP portfolio techniques.

Recent research in areas such as SAT solving and Integer Linear Programming has shown that the performances of a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. We report an empirical evaluation and comparison of portfolio approaches applied to Constraint Satisfaction Problems (CSPs). We compared models developed on top of off-the-shelf machine learning algorithms with respect to approaches used in the SAT field and adapted for CSPs, considering different portfolio sizes and using as evaluation metrics the number of solved problems and the time taken to solve them. Results indicate that the best SAT approaches have top performances also in the CSP field and are slightly more competitive than simple models built on top of classification algorithms.

The computing industry is currently facing a major architectural shift. Extra computing power is not coming anymore from higher processor frequencies, but from a growing number of computing cores and processors. For AI, and constraint solving in particular, this raises the question of how to scale current solving techniques to massively parallel architectures. While prior work focusses mostly on small scale parallel constraint solving, we conduct the first study on scalability of constraint solving on 100 processors and beyond in this paper. We propose techniques that are simple to apply and show empirically that they scale surprisingly well. These techniques establish a performance baseline for parallel constraint solving technologies against which more sophisticated parallel algorithms need to  compete  in the future.